Paper Citation Record · LEDGER

Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects

As of 22 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2505.20909.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.20909 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:48:35.908863Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

28 of 28 outbound references displayed

verified exact0
verified fuzzy19
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c348abd5-6460-4ca8-a46b-52eafef98d75 · outbound

This paper cites High- resolution image synthesis with latent diffusion models,.

Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects High- resolution image synthesis with latent diffusion models,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:40.404666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 26a7605a-4054-407f-981a-8ac79d29763b · outbound

This paper cites TRIP: Temporal Residual Learning with Image Noise Prior for Image-to-Video Diffusion Models,.

Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects TRIP: Temporal Residual Learning with Image Noise Prior for Image-to-Video Diffusion Models,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:40.134070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5bb18277-1c48-40a1-84c4-b4fe440dd5fd · outbound

This paper cites MotionPro: A Precise Motion Controller for Image-to-Video Generation,.

Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects MotionPro: A Precise Motion Controller for Image-to-Video Generation,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:39.876686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 22c6a66e-5de6-46e7-bde5-8fedde2ed8a1 · outbound

This paper cites An image is worth one word: Personalizing text-to-image generation using textual inversion,.

Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects An image is worth one word: Personalizing text-to-image generation using textual inversion,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:39.635937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 742c8803-9113-4b15-ac20-b7b8464716fc · outbound

This paper cites Dreambooth: Fine tuning text-to- image diffusion models for subject-driven generation,.

Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects Dreambooth: Fine tuning text-to- image diffusion models for subject-driven generation,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:39.367858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e4c96d64-81e2-435e-a482-890d2d36d444 · outbound

This paper cites Multi-concept customization of text-to-image diffusion,.

Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects Multi-concept customization of text-to-image diffusion,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:39.086919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 52aaa2b9-cb12-4240-bf55-86607696c232 · outbound

This paper cites Ssr-encoder: Encoding selective subject representation for subject-driven generation,.

Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects Ssr-encoder: Encoding selective subject representation for subject-driven generation,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:38.800091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8af1e7b6-2f03-410a-a01d-dce5a9be7c2e · outbound

This paper cites Blip-diffusion: Pre-trained subject representation for controllable text-to-image generation and editing,.

Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects Blip-diffusion: Pre-trained subject representation for controllable text-to-image generation and editing,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:38.550719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation eec0c16d-21bc-44c5-9bbe-506982e15ce7 · outbound

This paper cites Generative multimodal models are in- context learners,.

Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects Generative multimodal models are in- context learners,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:38.327398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c3a3fb72-17da-4008-a3fc-acab907f9fec · outbound

This paper cites Kosmos-G: Generating Images in Context with Multimodal Large Language Models.

Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects Kosmos-G: Generating Images in Context with Multimodal Large Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T13:48:34.203912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e96bcf18-40bc-4b62-b569-891094033f2d · outbound

This paper cites Instancediffusion: Instance-level control for image generation,.

Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects Instancediffusion: Instance-level control for image generation,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:38.028216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation edf20545-e900-4265-adb5-ac7574cac077 · outbound

This paper cites IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models.

Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T13:48:34.323871Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ffda3e5-f2de-4fe0-9615-ebf98a369428 · outbound

This paper cites $\lambda$-ECLIPSE: Multi-Concept Personalized Text-to-Image Diffusion Models by Leveraging CLIP Latent Space.

Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects $\lambda$-ECLIPSE: Multi-Concept Personalized Text-to-Image Diffusion Models by Leveraging CLIP Latent Space

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T13:48:34.424437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0bebb68c-7160-440d-9d48-2e403e5d86e2 · outbound

This paper cites Learning transferable visual models from natural language supervision,.

Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects Learning transferable visual models from natural language supervision,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:37.787586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8ffa62a7-eafc-4385-aba8-9c8474f19a33 · outbound

This paper cites AnyDoor: Zero-shot Object-level Image Customization.

Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects AnyDoor: Zero-shot Object-level Image Customization

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T13:48:34.680927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9fc5dca-64d6-4fd3-ac47-eb9cc2073c7c · outbound

This paper cites MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance.

Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T13:48:34.780116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7317e420-0fc7-42cd-99bd-24eacbb74771 · outbound

This paper cites Adding conditional control to text-to-image diffusion models,.

Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects Adding conditional control to text-to-image diffusion models,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T13:48:34.831272Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c3e7534-910f-4107-aa70-0f7f1c5e4c38 · outbound

This paper cites Layoutdiffusion: Controllable diffusion model for layout-to-image generation,.

Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects Layoutdiffusion: Controllable diffusion model for layout-to-image generation,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:37.563702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cd9dda3d-d91a-42f8-8de2-515769dda517 · outbound

This paper cites Gligen: Open-set grounded text-to-image generation,.

Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects Gligen: Open-set grounded text-to-image generation,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T13:48:35.031893Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d86ae8a-24c5-44b4-8b14-f28400fb0224 · outbound

This paper cites Training-free layout control with cross-attention guidance,.

Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects Training-free layout control with cross-attention guidance,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:37.384930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cbc2de7e-2525-41c2-86c7-f78454cc78be · outbound

This paper cites Boxdiff: Text-to-image synthesis with training-free box-constrained diffusion,.

Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects Boxdiff: Text-to-image synthesis with training-free box-constrained diffusion,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:37.184320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 362291ea-99af-4558-b3fa-446df2ac9510 · outbound

This paper cites Segment anything,.

Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects Segment anything,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:36.998320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d2560623-8c75-4ff0-b8ef-d8ba3a464216 · outbound

This paper cites Flamingo: a visual language model for few-shot learning,.

Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects Flamingo: a visual language model for few-shot learning,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:36.810479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f949f0bc-1185-4ac4-8c49-be374de90ac8 · outbound

This paper cites Moma: Multimodal llm adapter for fast personalized image generation,.

Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects Moma: Multimodal llm adapter for fast personalized image generation,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:36.566877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d3902ba2-2b04-482a-ab5c-c793de3bc6a4 · outbound

This paper cites Coco-stuff: Thing and stuff classes in context,.

Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects Coco-stuff: Thing and stuff classes in context,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:36.366437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c9d062ff-09f2-419f-bb80-5f8df0e6b607 · outbound

This paper cites The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale,.

Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:36.219074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 95a8d426-622c-4254-a8c7-3bb79f77ee92 · outbound

This paper cites LW-DETR: A Transformer Replacement to YOLO for Real-Time Detection.

Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects LW-DETR: A Transformer Replacement to YOLO for Real-Time Detection

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T13:48:35.843367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0dd19e8-53fd-41ef-93e8-0723dbe878f5 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects DINOv2: Learning Robust Visual Features without Supervision

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T13:48:35.908863Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.